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Description

Recent work in structure from motion (SfM) has successfully built 3D models from large unstructured collections
of images downloaded from the Internet. Most approaches
use incremental algorithms that solve progressively larger
bundle adjustment problems. These incremental techniques
scale poorly as the number of images grows, and can drift
or fall into bad local minima. We present an alternative formulation for SfM based on ?nding a coarse initial solution
using a hybrid discrete-continuous optimization, and then
improving that solution using bundle adjustment. The initial optimization step uses a discrete Markov random ?eld
(MRF) formulation, coupled with a continuous LevenbergMarquardt re?nement. The formulation naturally incorporates various sources of information about both the cameras
and the points, including noisy geotags and vanishing point
estimates. We test our method on several large-scale photo
collections, including one with measured camera positions,
and show that it can produce models that are similar to or
better than those produced with incremental bundle adjustment, but more robustly and in a fraction of the time.